Traction control based on friction coefficent estimation

ABSTRACT

Method and apparatus are disclosed for traction control based on friction coefficient estimation. An example vehicle includes a plurality of sensors to measure qualities of a surface of a road and an anti-lock brake system module. The anti-lock brake system module (a) estimates confidence values for different road surface types based on the qualities of the surface of the road, (b) estimates a coefficient of friction between the road and tires of the vehicle based on the confidence values, and (c) adapt a traction control system by altering a target slip based on the coefficient of friction.

TECHNICAL FIELD

The present disclosure generally relates to traction control systems ina vehicle and, more specifically, traction control based on frictioncoefficient estimation.

BACKGROUND

A stability and traction control system detects a loss of traction ondriving wheel. This is often caused by engine torque and throttle inputbeing mismatched to road condition. The stability and traction controlsystem applies brakes to that wheel so it is not spinning faster thanthe other wheels. However, when the stability and traction controlsystem does not take into account the environment that it is drivingthrough, the system reacts to slipping instead of proactively managingand anticipating a loss of traction.

SUMMARY

The appended claims define this application. The present disclosuresummarizes aspects of the embodiments and should not be used to limitthe claims. Other implementations are contemplated in accordance withthe techniques described herein, as will be apparent to one havingordinary skill in the art upon examination of the following drawings anddetailed description, and these implementations are intended to bewithin the scope of this application.

Method and apparatus are disclosed for traction control based onfriction coefficient estimation. An example vehicle includes a pluralityof sensors to measure qualities of a surface of a road and an anti-lockbrake system module. The anti-lock brake system module (a) estimatesconfidence values for different road surface types based on thequalities of the surface of the road, (b) estimates a coefficient offriction between the road and tires of the vehicle based on theconfidence values, and (c) adapt a traction control system by altering atarget slip based on the coefficient of friction.

A method includes measuring qualities of a surface of a road ahead of avehicle with a first sensor and a second sensor different than the firstsensor. The method also includes (a) generating first confidence valuesfor different road surface types based on the qualities of the surfaceof the road measured by the first sensor, and (b) generating secondconfidence values for the different road surface types based on thequalities of the surface of the road measured by the second sensor.Additionally, the method includes estimating a coefficient of frictionbetween the road and tires of die vehicle based on an aggregate of thefirst and second confidence values. The method includes controlling,with an anti-lock brake system, torque applied to wheels of the vehiclebased on the coefficient of friction.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the invention, reference may be made toembodiments shown in the following drawings. The components in thedrawings are not necessarily to scale and related elements may beomitted, or in some instances proportions may have been exaggerated, soas to emphasize and clearly illustrate the novel features describedherein. In addition, system components can be variously arranged, asknown in the art. Further, in the drawings, like reference numeralsdesignate corresponding pans throughout the several views.

FIG. 1 illustrates a vehicle operating in accordance with the teachingsof this disclosure.

FIG. 2 is a block diagram of a friction estimator of the tractioncontrol system of the vehicle of FIG. 1.

FIG. 3 is a block diagram of the electronic components of the vehicle ofFIG. 1.

FIG. 4 is a flowchart of a method to control the traction control systembases on an estimated friction, which may be implemented by theelectronic components of FIG. 3.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

While the invention may be embodied in various forms, there are shown inthe drawings, and will hereinafter be described, some exemplary andnon-limiting embodiments, with the understanding that the presentdisclosure is to be considered an exemplification of the invention andis not intended to limit the invention to the specific embodimentsillustrated.

As used herein, a traction control-based system refers to any vehiclesystem that controls the force applied by the wheels of the vehicle to asurface of a road. Example traction control-based systems includetraction control systems, electronic stability systems, roll stabilitysystems, etc. The coefficient of friction between the tires of a vehicleand the surface of the road is determined by the characteristics of thetire (e.g., the grip of the tire, the surface area of the tire/roadinterface, etc.) and the characteristics of the surface of the road. Forexample, the coefficient of friction between the tires and an icy or wetroad is lower than the coefficient of friction on dry asphalt. Asdisclosed below, with an estimated coefficient of friction, the tractioncontrol-based system adapts a slip target to control the force of thewheels on the road in response to a change of friction environment. Forexample, the traction control system may control the torque of thewheels differently when the road surface is ice than when the roadsurface is covered in gravel. Slip is a ratio of vehicle velocity towheel spin velocity. For example, when the vehicle velocity and wheelspin velocity are equal, the slip is 0% and when the vehicle velocity iszero and the wheel spin velocity is greater than zero, the slip is 100%.A slip of 100% means that the vehicle is not moving despite the wheelspinning. Different surfaces have different idea slip amounts for thevehicle to move safely on the surface (e.g., based on the coefficient offriction). For example, a vehicle may get stuck in sand if there in notat least some slip. Traditionally, for example, tractions controlsystems work to eliminate all slip not knowing what the coefficient offriction. This, in such a scenario, the vehicle would not move in sand.The traction control-based system of this disclosure sets a target slipbased on the coefficient of friction. The traction control-based systemuses one or more methods to estimate the coefficient between the tiresof a vehicle and the ground that the vehicle is currently traversing oris projected to traverse based on the vehicle's current trajectory. Whenmultiple methods are used, the results of each method are multiplied byconfidence factors to produce confidence estimates of the possible roadconditions. The higher confidence estimates are given more weight whenestimating the coefficient of friction in the path of the vehicle.

One method involves analyzing a signal from a sensor (e.g., measuringsuspension oscillation, road noise, tire pressure, etc.) to determinewhether the signal is characteristic of the vehicle driving over aparticular road surface. With this method, the traction control systemapplies several filters designed to filter out signals from a particularroad condition (e.g., asphalt, snow, mud, gravel, etc.). The amount thata particular filter makes a signal smooth (e.g., attenuates) increasesthe confidence that the vehicle is driving on the corresponding roadtype. For example, when a filter tuned to a gravel surface causes thesignal to become substantially smooth, then the traction control systemmay be confident that the vehicle is traveling on gravel.

Another method involves analyzing visual data from cameras (e.g., astandard camera, an RGB camera, an infrared camera, etc.) and comparesthe reflectivity, color, and smoothness of the images against controlimages for different surfaces. The confidence that the vehicle istraveling on a particular surface is related to the amount that theimages matches a control image of that surface. For example, an image ofa white reflective surface may match a control image of a snowy surface.This method facilitates determining a quality of the surface bydifferent sizes of features. For example, the method may distinguishsmall grains from large grains to differentiate between sandy or gravelsurfaces from tile or rocky surfaces. Additionally, with this method,the vehicle may construct luminosity maps to determine the nature of thesurface. Different luminosity values (p) are indicative of differentsurfaces. For example, dry asphalt may have one characteristicluminosity value while wet asphalt may have another luminosity value. Ameasured luminosity is compared to a table that indicates confidences ofdie different types of road surfaces give the input luminosity.

In another method, the vehicle uses range detection sensors (e.g.,LiDAR, RADAR, ultrasonic, etc.) to broadcast wave signals at regularintervals. The wave signals reflect and refract off of the surface. Thevehicle analyzes the pattern of the reflections and/or refractions todetermine the type of surface. For example, using this method, thevehicle may determine whether the surface of the road is smooth (e.g.,indicative of asphalt, ice, etc.) or rough (e.g., indicative of gravelor snow).

In another method, the vehicle analyzes changes in steering current drawat a constant speed. When the steering current draw increases, thevehicle may determine that the surface corresponding with a surface witha higher amount of traction and vice versa.

Additionally, in some examples, the vehicle also analyzes environmentaldata (e.g., weather data, ambient temperature data, humidity data,precipitation data, etc.) to alter the confidence levels of certain roadtypes. For example, the environmental data indicative that is it coldoutside may increase the confidence levels that the road surface is snowor ice.

As described below, the vehicle uses one or more of these methods toestimate the surface type of the road on which the vehicle is driving.In some examples, the uses multiple methods to generate confidencelevels for multiple surface types and selects one of the surface typesfor the road based on which surface type has the highest confidencelevel. For example, the wave signal may indicate that the surface of theroad is reflective, the signal from one of the sensors indicate thevehicle is traveling on asphalt, and the environmental data indicatesthat it has rained within the last 24 hours, the confidence levels mayindicate that the vehicle is traveling on wet asphalt.

FIG. 1 illustrates a vehicle 100 operating in accordance with theteachings of this disclosure. The vehicle 100 may be a standard gasolinepowered vehicle, a hybrid vehicle, an electric vehicle, a fuel cellvehicle, and/or any other mobility implement type of vehicle. Thevehicle 100 includes parts related to mobility, such as a powertrainwith an engine, a transmission, a suspension, a drives haft, and/orwheels, etc. The vehicle 100 may be non-autonomous, semi-autonomous(e.g., some routine motive functions controlled by the vehicle 100), orautonomous (e.g., motive functions are controlled by the vehicle 100without direct driver input). In the illustrated example the vehicle 100includes sensors 102, an on-board communications module (OBCM) 104, anda anti-lock brake system (ABS) module 106.

The sensors measure properties of and around the vehicle 100. Thesensors 102 may be arranged in and around the vehicle 100 in anysuitable fashion. The sensors 102 may mounted to measure propertiesaround the exterior of the vehicle 100. For examples, such sensors 102may include cameras (e.g., a standard camera, an RGB camera, an infraredcamera) and range detection sensors (e.g., RADAR, LiDAT, ultrasonic,etc.), etc. Additionally, some sensors 102 may be mounted inside thecabin of the vehicle 100 or in the body of the vehicle 100 (such as, theengine compartment, the wheel wells, etc.) to measure properties in theinterior of the vehicle 100. For example, such sensors 102 may includeaccelerometers, odometers, tachometers, pitch and yaw sensors, wheelspeed sensors, microphones, tire pressure sensors, biometric sensors,suspension vibration sensors, etc. These sensors 102 produce signalsthat may be analyzed to determine the type of surface that the vehicle100 is currently traveling on.

The on-board communications module 104 includes wired or wirelessnetwork interfaces to enable communication with external networks 108.The on-board communications module 104 also includes hardware (e.g.,processors, memory, storage, antenna, etc.) and software to control thewired or wireless network interfaces. In the illustrated example, theon-board communications module 104 includes one or more communicationcontrollers for standards-based networks (e.g., Global System for MobileCommunications (GSM), Universal Mobile Telecommunications System (UMTS),Long Term Evolution (LTE), Code Division Multiple Access (CDMA), WiMAX(IEEE 802.16m); local area wireless network (including IEEE 802.11a/b/g/n/ac or others), dedicated short range communication (DSRC), andWireless Gigabit (IEEE 802.1 lad), etc.). In some examples, the on-boardcommunications module 104 includes a wired or wireless interface (e.g.,an auxiliary port, a Universal Serial Bus (USB) port, a Bluetoothwireless node, etc.) to communicatively couple with a mobile device(e.g., a smart phone, a smart watch, a tablet, a police mobile computer,etc.). In such examples, the on-board communications module 104 maycommunicated with the external network 108 via the coupled mobiledevice.

The external network(s) 108 may be a public network, such as theInternet; a private network, such as an intranet; or combinationsthereof, and may utilize a variety of networking protocols now availableor later developed including, but not limited to, TCP/I P basednetworking protocols. In the illustrated example, the external network108 includes a weather server 110. The vehicle 100 receivesenvironmental data (e.g., weather data, ambient temperature data,humidity data, precipitation data, etc.) from the weather server 110.

The anti-lock brake system module 106 controls the brakes of the vehicle100 to control the torque applied to each of the wheels. The anti-lockbrake system module 106 includes a stability and traction control systemthat detects (e.g., via mismatches in the measurements of the wheelspeed sensors) when one or more wheels loose traction. In theillustrated example, the anti-lock brake system module 106 includes afriction estimator 112. As disclosed in connection with FIG. 2 below,the friction estimator 112 estimates a coefficient of friction betweenthe tires of the vehicle 100 and the road. The anti-lock brake systemmodule 106 uses the estimated coefficient of friction alter the targetslip of the traction control-based systems to the wheels to reduce thefrequency that one or more wheel lose traction to cause an adversedriving event (e.g., getting stuck, fishtailing, etc.) considering thesurface on which the vehicle 100 is driving.

FIG. 2 is a block diagram of the friction estimator 112 of the tractioncontrol system of the vehicle 100 of FIG. 1. In the illustrated example,the friction estimator 112 receives vehicle operations data and/orimages from various sensors 102 and environmental data from the weatherserver 110 (e.g., via the on-board communications module 104). Thefriction estimator 112 includes one or more coefficient generators 202a-202 d. The coefficient generators 202 a-202 d generate confidencevalues associated with different road types based on the vehicleoperations data from the sensors 102. For example, one of thecoefficient generators 202 a-202 d may generate confidence values thatthe surface is 90% likely to be dry asphalt, 7% likely to be wetasphalt, and 3% likely to be gravel. Hie coefficient generators 202a-202 d include reference tables 204 a-204 e that correlate the vehicleoperations data with known samples to produce the confidence levels. Forexample, one coefficient generator 202 a-202 d may include referenceimages in its reference table 204 a-204 d to compare images captured byan RGB camera to the reference images to determine the confidence valuesof the types of road surfaces. A confidence generator 206 compiles theconfidence values from the coefficient generators 202 a-202 d andestimates a coefficient of friction based on the confidence levels. Insome examples, the confidence generator 206 selects the type of roadsurface that has the highest aggregate confidence value. In someexamples, the confidence generator 206 includes a lookup table thatassociated types of road surfaces with coefficients of friction.

The filter coefficient generator 202 a receives a signal from thevehicle operations data and individually applies a plurality of filtersto the signal. In some examples, the signal is from a suspensionvibration sensors and/or a tire pressure monitoring system (TPMS). Insome examples, the filter coefficient generator 202 a appliespre-filters to the signal to filter out normal vehicle behaviors (e.g.,turning engine vibration, wheel speed, etc.) and environmental factors(e.g. variation based on wind, rains, etc.). Each filter is designed toattenuate the signal that exhibits characteristics of a particular typeof road condition. For example, the one filter may be designed toattenuate signals generated by the vehicle 100 traversing gravel andanother filter may be designed to attenuate signals generated by thevehicle 100 traversing asphalt. The filter coefficient generator 202 aselects confidence values for different road types based on the amountthat the signal is attenuated.

The image coefficient generator 202 b compares images captured by thecameras of the vehicle 100 to reference images to determine theconfidence values for the different types of mad surfaces. In someexamples, the camera(a) capture images of the road ahead of the vehicle100 (e.g., two feet ahead, four feet ahead, etc.) based on the currenttrajectory of the vehicle 100. The confidence values are based on thepercentage that an image from the cameras matches one of the referenceimages. For example, the reference table 204 b may include one or moreimages of each of grassy surfaces, dry asphalt, surfaces, wet asphaltsurfaces, snowy surfaces, icy surfaces, muddy surfaces, and/or gravelsurfaces, etc. In some examples, the image coefficient generator 202 bbases the confidence values based on sizes of features identified in theimages. For example, the image coefficient generator 202 b may determinethat images with small features are more likely to contain gravel, sand,or dirt while images with larger features are more likely to be tiles orrocks, etc. In some examples, the image coefficient generator 202 b alsouses color analysis to determine likelihoods that the surface of theroad is a particular type of road surface. For example, when the imageindicates that the road surface has few or no distinguishable features(e.g., the road surface is asphalt, concrete, snow, or mud, etc.), theimage coefficient generator 202 b may use the color of the surface todetermine confidence values.

In some examples, the image coefficient generator 202 b maps (e.g., viaLiDAR, etc.) the reflective surfaces in an image or a series of images.Shimmering or moving bright spots are indicative of the surface of theroad being wet. A non-moving speckled surface is indicative of a roughsurface. A consistently bright surface is indicative of a homogeneoussurface, such as ice, snow, or mud. In some examples, the referencetable 204 b associates luminosity measurements to likely types of roadsurfaces.

The reflection coefficient generator 202 c analyze wave patternsbroadcast by the range detection sensors to determine the degree thatthe surface is rough or smooth. As the wave signals are broadcast fromthe range detection sensors, the wave reflect and refract off thesurface in question and some waves return to the sensor. Thereflecting/refracting signals are analyzed to determine confidencelevels for the various types of surfaces. For example, a smooth surfacemay have relatively high confidence levels for asphalt, ice, and snow;while rough surfaces may have relatively high confidence level ofgravel, etc.

The current coefficient generator 202 d analyzes change in steeringcurrent draw (e.g., from a current sensor in an electronic powersteering (EPS) system, etc.) when the vehicle is traveling at anapproximately constance speed. For example, when the current coefficientgenerator 202 d detects the steering control unit drawing more currentto turn the wheel, the current coefficient generator 202 d determinesthat the surface on which the vehicle 100 is driving is rough. Thereference table 204 d associates current draw with confidence levels forthe various types of road surfaces.

An environment detector 208 receives environmental data via the on-boardcommunications module 104. The environment detector 208 generatesweights to apply to the confidence values generated by the coefficientgenerators 202 a-202 d based on road conditions proximate the vehicle100 based on the weather data. For example, for a threshold period oftime after it has rained, the environment detector 208 may generateweighting factors for types of road surfaces associated with “wetness”(e.g. mud, wet asphalt, etc.) that increase the corresponding confidencevalues and weighting factors for types of road surfaces associated with“dryness” (e.g., dry asphalt, dirt, etc.) that decrease correspondingthat decrease the corresponding confidence values. The weighing factorsmay be influenced by other environmental factors such as elevation, roadtrajectory, and recent temperature history. For example, rain on a dirtroad will result in weighing factors increase confidence values of mudin valleys due to the formation of likely formation of mud; snow falloutside when the ambient temperature is below freezing will result inweighing factors increasing confidence values related to snow due to thelikely accumulation of snow on the road. As another example, theenvironment detector 208 may use snow plow data may further refine thepredicted condition of the road.

FIG. 3 is a block diagram of the electronic components 300 of thevehicle 100 of FIG. 1. In the illustrated example, the electroniccomponents 300 include the sensors 102, the on-board communicationsmodule 104, the anti-lock brake system module 106, and a vehicle databus 302.

The anti-lock brake system module 106 includes a processor or controller304 and memory 306. In the illustrated example, the anti-lock brakesystem module 106 is structured to include friction estimator 112. Theprocessor or controller 304 may be any suitable processing device or setof processing devices such as, but not limited to: a microprocessor, amicrocontroller-based platform, a suitable integrated circuit, one ormore field programmable gate arrays (FPGAs), and/or one or moreapplication-specific integrated circuits (ASICs). The memory 306 may bevolatile memory (e.g., RAM, which can include non-volatile RAM, magneticRAM, ferroelectric RAM, and any other suitable forms); non-volatilememory (e.g., disk memory, FLASH memory, EPROMs. EEPROMs, non-volatilesolid-state memory, etc.), unalterable memory (e.g., EPROMs), read-onlymemory, and/or high-capacity storage devices (e.g., hard drives, solidstate drives, etc). In some examples, the memory 306 includes multiplekinds of memory, particularly volatile memory and non-volatile memory.

The memory 306 is computer readable media on which one or more sets ofinstructions, such as the software for operating the methods of thepresent disclosure can be embedded. The instructions may embody one ormore of the methods or logic as described herein. In a particularembodiment, the instructions may reside completely, or at leastpartially, within any one or more of the memory 306, the computerreadable medium, and/or within the processor 304 during execution of theinstructions.

The terms “non-transitory computer-readable medium” and “tangiblecomputer-readable medium” should be understood to include a singlemedium or multiple media, such as a centralized or distributed database,and/or associated caches and servers that store one or more sets ofinstructions. The terms “non-transitory computer-readable medium” and“tangible computer-readable medium” also include any tangible mediumthat is capable of storing, encoding or carrying a set of instructionsfor execution by a processor or that cause a system to perform any oneor more of the methods or operations disclosed herein. As used herein,the term “tangible computer readable medium” is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals.

In die illustrated example, the vehicle data bus 302 communicativelycouples the on-board communications module 104 and the anti-lock brakesystem module 106. In some examples, the vehicle data bus 302 includesone or more data buses. The vehicle data bus 302 may be implemented inaccordance with a controller area network (CAN) bus protocol as definedby International Standards Organization (ISO) 11898-1, a Media OrientedSystems Transport (MOST) bus protocol, a CAN flexible data (CAN-FD) busprotocol (ISO 11898-7) and/a K-line bus protocol (ISO 9141 and ISO14230-1), and/or an Ethernet™ bus protocol IEEE 802.3 (2002 onwards),etc.

FIG. 4 is a flowchart of a method to control the anti-lock brake systembased on an estimated friction, which may be implemented by theelectronic components 300 of FIG. 3. Initially, at block 402, thefriction estimator 112 selects the next coefficient generator 202 a-202d. At block 404, the friction estimator 112 takes measurements withsensors 102 associated with the selected coefficient generator 202 a-202d. For example, when the image coefficient generator 202 b is selected,the friction estimator 112 captures images from the camera(s). At block406, the friction estimator 112 analyzes the measurements taken at block404 to generate confidence values for the types of road surfaces. Atblock 408, the friction estimator 112 determines whether there isanother coefficient generator 202 a-202 d to select. If there is anothercoefficient generator 202 a-202 d to select, the method returns to block402. Otherwise, when there is not another coefficient, generator 202a-202 d to select, the method continues to block 410. At block 410, thefriction estimator 112 calculates aggregate confidence values for thetypes of road surfaces. In some examples, the friction estimator 112applies weighing factors to the confidence values based on environmentalor luminosity data. At block 412, the friction estimator 112 determineswhether the aggregate confidence value associated with any of the typesof road surfaces satisfies (e.g., is greater than or equal to) aconfidence threshold. For example, the confidence threshold may be 70%.When the whether the aggregate confidence value associated with any ofthe types of road surfaces satisfy the confidence threshold, the methodcontinues at block 416. Otherwise, when the whether the aggregateconfidence value associated with none of the types of road surfacessatisfy the confidence threshold, the method continues at block 414.

At block 414, the anti-lock brake system module 106 controls thetraction control system, rollover control system and/or the stabilitycontrol system based on a default target slip (e.g., 0% slip, etc.). Atblock 416, the friction estimator 112 estimates the friction between theroad surface and the tires of the vehicle 100 based on the aggregateconfidence values for the types of road surfaces. At block 418, theanti-lock brake system module 106 controls the traction control system,rollover control system and/or the stability control system based on theestimated coefficient of friction. For example, the anti-lock brakesystem module 106 may change the torque applied to one or more wheels,change the relationship between the input of the acceleration pedal andthe delivered torque, and/or the wheel slip (e.g., as represented by theslip target, etc.).

The flowchart of FIG. 4 is representative of machine readableinstructions stored in memory (such as the memory 306 of FIG. 3) thatcomprise one or more programs that, when executed by a processor (suchas the processor 304 of FIG. 3), cause the vehicle 100 to implement theexample friction estimator 112 and/or more generally, the exampleanti-lock brake system module 106 of FIGS. 1, 2, and 3. Further,although the example program(s) is/are described with reference to theflowchart illustrated in FIG. 4, many other methods of implementing theexample friction estimator 112 and/or more generally, the exampleanti-lock brake system module 106 may alternatively be used. Forexample, the order of execution of the blocks may be changed, and/orsome of the blocks described may be changed, eliminated, or combined.

In this application, the use of the disjunctive is intended to includethe conjunctive. The use of definite or indefinite articles is notintended to indicate cardinality. In particular, a reference to “the”object or “a” and “an” object is intended to denote also one of apossible plurality of such objects. Further, the conjunction “or” may beused to convey features that are simultaneously present instead ofmutually exclusive alternatives. In other words, the conjunction “or”should be understood to include “and/or”. As used here, the terms“module” and “unit” refer to hardware with circuitry to providecommunication, control and/or monitoring capabilities, often inconjunction with sensors. “Modules” and “units” may also includefirmware that executes on the circuitry. The terms “includes,”“including,” and “include” are inclusive and have the same scope as“comprises” “comprising,” and “comprise” respectively.

The above-described embodiments, and particularly any “preferred”embodiments, are possible examples of implementations and merely setforth for a clear understanding of the principles of the invention. Manyvariations and modifications may be made to the above-describedembodiment(s) without substantially departing from the spirit andprinciples of the techniques described herein. All modifications areintended to be included herein within the scope of this disclosure andprotected by the following claims.

1-15. (canceled)
 16. A method comprising: capturing a series of imagesof a surface of a road ahead of a vehicle using a first sensor;determining luminosity values and changes of the luminosity valueswithin the series of images; comparing the luminosity values and changesof the luminosity values to values stored in a table; generating, with aprocessor in a vehicle, a first confidence value that the road thevehicle is on is of a first road surface type based on the comparisonbetween the luminosity values and changes of the luminosity values tovalues stored in the table; estimating a coefficient of friction betweenthe road and tires of the vehicle based on the first confidence value;and controlling, with an anti-lock brake system, torque applied towheels of the vehicle based on the coefficient of friction.
 17. Themethod of claim 16, wherein the first sensor is one of a camera, asuspension vibration sensor, or an accelerometer.
 18. The method ofclaim 17, wherein generating the first confidence value includes:capturing an image of a surface of a road ahead of the vehicle; andcomparing a size of features found within the image to the size offeatures found within a reference image of the first road surface type.19. A method comprising: determining a steering current draw of avehicle; estimating a first confidence value that a road the vehicle ison is a road surface of a first road surface type based on the steeringcurrent draw of the vehicle; estimating a coefficient of frictionbetween the road surface and tires of the vehicle based on the firstconfidence value; and controlling wheels of the vehicle by altering atarget slip based on the coefficient of friction.